Application of Variations of Cohort Intelligence in Designing Fractional PID Controller for Various Systems Pritesh Shah and Anand J. Kulkarni Abstract The socio-inspired algorithm is widely used for engineering applications. Recently, Cohort intelligence (CI) algorithm, a socio-inspired algorithm has been applied to various control systems controlled by fractional order controller. The Cohort intelligence algorithm has already been successfully applied in unconstrained test problems, various mechanical applications, combinatorial problem such as 0–1 Knapsack Problem, healthcare domain, practical applications of multiple Knapsack problems and selection of cross-border shippers problem. In this book chapter, vari- ations of cohort intelligence will be applied for the various control system including first-order system, second-order system, fractional-order system, and higher order systems. Optimization algorithms are used for the design of various controllers like the classical PID controller, MPC controller, fractional-order controller, and various model-based controllers. Also, these algorithms can be used to estimate the param- eters of various systems to model them. Various optimization techniques have been applied for designing controllers like genetic algorithm, particle swarm optimization (PSO), electromagnetism-like algorithm, improved differential evolution, etc. Most of these methods are not able to find global optimal solution for the given plant. Besides, these methods don’t properly tune for all varieties of systems. Variation of CI algorithm can be applied to different types of control system problems. Keywords Cohort intelligence · Fractional PID controller · Fractional calculus · Socio-inspired optimization P. Shah (B ) · A. J. Kulkarni Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India e-mail: pritesh.ic@gmail.com A. J. Kulkarni Odette School of Business, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B3P4, Canada e-mail: anand.kulkarni@sitpune.edu.in © Springer Nature Singapore Pte Ltd. 2019 A. J. Kulkarni et al. (eds.), Socio-cultural Inspired Metaheuristics, Studies in Computational Intelligence 828, https://doi.org/10.1007/978-981-13-6569-0_9 175